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Towards Automated Pain Detection in Children Using Facial and Electrodermal Activity

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Artificial Intelligence in Health (AIH 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11326))

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Abstract

Accurately determining pain levels in children is difficult, even for trained professionals and parents. Facial activity and electrodermal activity (EDA) provide rich information about pain, and both have been used in automated pain detection. In this paper, we discuss preliminary steps towards fusing models trained on video and EDA features respectively. We compare fusion models using original video features and those using transferred video features which are less sensitive to environmental changes. We demonstrate the benefit of the fusion and the transferred video features with a special test case involving domain adaptation and improved performance relative to using EDA and video features alone.

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Acknowledgments

This work was supported by National Institutes of Health National Institute of Nursing Research grant R01 NR013500 and NSF IIS 1528214.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, UC San Diego, La Jolla, CA, USA

    Xiaojing Xu

  2. Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA

    Büşra Tuğçe Susam & Murat Akcakaya

  3. Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA

    Hooman Nezamfar

  4. Rady Childrens Hospital and Department of Pediatrics, UC San Diego, La Jolla, CA, USA

    Damaris Diaz & Jeannie S. Huang

  5. Department of Psychology, University of British Columbia, Vancouver, BC, Canada

    Kenneth D. Craig

  6. Department of Health Sciences, Northeastern University, Boston, MA, USA

    Matthew S. Goodwin

  7. Department of Cognitive Science, UC San Diego, La Jolla, CA, USA

    Virginia R. de Sa

Authors
  1. Xiaojing Xu
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  2. Büşra Tuğçe Susam
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  3. Hooman Nezamfar
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  4. Damaris Diaz
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  5. Kenneth D. Craig
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  6. Matthew S. Goodwin
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  7. Murat Akcakaya
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  8. Jeannie S. Huang
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  9. Virginia R. de Sa
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Corresponding author

Correspondence to Xiaojing Xu .

Editor information

Editors and Affiliations

  1. University of Melbourne, Melbourne, VIC, Australia

    Fernando Koch

  2. IIIA-CSIC and Autonomous University of Barcelona, Barcelona, Spain

    Andrew Koster

  3. Universitat Rovira i Virgili, Tarragona, Spain

    David Riaño

  4. Università di Bologna, Cesena, Italy

    Sara Montagna

  5. HES-SO Valais Wallis, Sierre, Switzerland

    Michael Schumacher

  6. VU University Amsterdam, Amsterdam, The Netherlands

    Annette ten Teije

  7. Karolinska Institute, Stockholm, Sweden

    Christian Guttmann

  8. University of Ulm, Ulm, Germany

    Manfred Reichert

  9. State University of New York, Oswego, NY, USA

    Isabelle Bichindaritz

  10. Imperial College London, London, UK

    Pau Herrero

  11. University of Erlangen and Nuremberg, Erlangen, Germany

    Richard Lenz

  12. University of Girona, Girona, Spain

    Beatriz López

  13. Ohio University, Athens, OH, USA

    Cindy Marling

  14. Oxford Brookes University, Oxford, UK

    Clare Martin

  15. Università del Piemonte Orientale, Alessandria, Italy

    Stefania Montani

  16. School of Computing Science and Digital Media, Robert Gordon University, Aberdeen, UK

    Nirmalie Wiratunga

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Xu, X. et al. (2019). Towards Automated Pain Detection in Children Using Facial and Electrodermal Activity. In: Koch, F., et al. Artificial Intelligence in Health. AIH 2018. Lecture Notes in Computer Science(), vol 11326. Springer, Cham. https://doi.org/10.1007/978-3-030-12738-1_13

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  • DOI: https://doi.org/10.1007/978-3-030-12738-1_13

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-12737-4

  • Online ISBN: 978-3-030-12738-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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